I came across a link recently to an interview with Richard Nisbett arguing against multiple regression analysis in social psychology. His arguments have great relevance to epidemiology - what he's arguing against is not just regression, but observational science in general. The punchline is that correlation is not causation, an observation popular enough to have spawned entire blogs.

"A huge range of science projects are done with multiple regression analysis. The results are often somewhere between meaningless and quite damaging." - Richard Nisbett

Research epidemiology gets around this trap by using case control studies and randomized controlled trials. These trials take time and money though, and for certain problems (including most in my subfield, which prioritizes speed), an interim solution is needed. As Alessandro Vespignani says, "start where you are, use what you have, do what you can." Sometimes observational studies (and regression) are part of that.

I can't give a total pass though. I see linear and logistic regression used as hammers for every nail. There are many other methods for regression and classification that need more attention in the epi world. This Quora post has a nice assembly of terms to google, and the Kaggle blog is a great resource for learning what others have used for different problems. I don't expect all epidemiologists to be statisticians or data scientists, but having a few extra tools at your disposal never hurts.